2  Course Outline

Author
Affiliation

Dr Randy Johnson

Hood College

Published

September 25, 2025

Course Description

BIFX 504 Advanced Molecular Bio for Bioinformatics

Prerequisite: BIFX 501 or waiver of BIFX 501 or permission of instructor. The accelerated use of next generation sequencing means that the analysis of sequencing data is one of primary job duties of most bioinformaticians. The DNA/RNA sequencing boom is now being followed by a renewed focus on metagenomics as well as high-throughput protein sequencing as analytical techniques. In this course, students will gain detailed knowledge of the biology that underlies these and other techniques. By understanding the full range of transcripts made by cells, the mechanisms that regulate transcription, and the details of RNA transcript processing and translation, students will learn how the underlying biology affects the sensitivity and correct interpretation of key types of bioinformatics assays, including microarrays, genome-wide association studies, and sequencing of DNA, RNA, proteins, and the microbiome. The fundamentals of good experimental design will be emphasized throughout the course.

Course objectives

  1. Establish a foundational understanding by researching the core principles of molecular biology essential for bioinformaticians, including the central dogma, gene structure, and regulation.
  2. Investigate the evolution and current landscape of DNA sequencing technologies, focusing on the principles behind Next-Generation Sequencing (NGS) platforms and their applications in genomics.
  3. Explore the primary bioinformatics workflows for processing genomic data, such as genome assembly, annotation of genes and regulatory elements, and the identification of genetic variants.
  4. Research the field of transcriptomics to understand how gene expression is quantified and analyzed.
    • Examine bulk RNA-sequencing for studying gene expression across a population of cells.
    • Investigate single-cell RNA-sequencing to analyze cellular heterogeneity and define cell types.
  5. Survey the field of epigenomics by finding information on methods like ChIP-seq and ATAC-seq, which are used to study DNA modifications and chromatin structure.
  6. Examine the principles and technologies behind proteomics and metabolomics.
    • Research the use of mass spectrometry for large-scale protein identification and quantification.
    • Find information on techniques for profiling small molecule metabolites to understand cellular metabolic states.
  7. Analyze the methods and applications of metagenomics for studying the genetic material recovered directly from microbial communities in environmental samples.
  8. Synthesize the information by researching integrative and applied bioinformatics topics, including systems biology for modeling biological networks and the role of bioinformatics in clinical diagnostics and personalized medicine.

Weekly topics

Foundational Concepts

  1. Introduction to Bioinformatics and Biological Databases This topic will introduce the field of bioinformatics, its applications in molecular biology, and the central dogma. It will also cover the major biological databases, such as NCBI, GenBank, and UniProt, and how to navigate and retrieve data from them.

  2. Sequence Alignment and Database Searching This fundamental topic will cover the theory and algorithms behind pairwise and multiple sequence alignment. Students will learn how to use tools like BLAST and Clustal to compare sequences, identify homologous genes, and infer evolutionary relationships.

Genomics

  1. Next-Generation Sequencing (NGS) Technologies This section will provide a detailed overview of the various NGS platforms (e.g., Illumina, PacBio, Oxford Nanopore) and their underlying chemistry. It will cover library preparation, sequencing principles, and the generation of raw sequencing data.

  2. Genome Assembly and Annotation Following the introduction to NGS, this topic will focus on the computational methods used to assemble raw sequencing reads into a complete genome. It will also cover the process of genome annotation, which is the identification of genes, regulatory elements, and other important features within a genome.

Transcriptomics

  1. Bulk RNA-Seq Analysis This topic will introduce the principles of transcriptomics and its application in quantifying gene expression. Students will learn the entire workflow for bulk RNA-seq data analysis, from quality control and read mapping to differential gene expression analysis and data visualization.

  2. Single-Cell RNA-Seq (scRNA-Seq) Analysis Building on the concepts of RNA-seq, this section will delve into the analysis of gene expression at the single-cell level. It will cover the unique computational challenges of scRNA-Seq data, including normalization, dimensionality reduction, and cell clustering to identify different cell types and states.

Proteomics and Metabolomics

  1. Proteomics and Mass Spectrometry This topic will introduce the field of proteomics, the large-scale study of proteins. It will cover the basics of mass spectrometry, a key technology in proteomics, and the bioinformatics pipelines used to identify and quantify proteins from complex biological samples.

  2. Structural Bioinformatics and Protein Modeling Here, the focus will be on the three-dimensional structures of proteins. Students will learn about protein structure databases (e.g., PDB), visualization tools (e.g., PyMOL), and computational methods for predicting protein structure and function.

  3. Metabolomics and Pathway Analysis This section will introduce metabolomics, the study of small molecules (metabolites) in a biological system. It will cover techniques for metabolite profiling and the use of bioinformatics tools to perform pathway analysis, which helps in understanding the functional implications of changes in gene and protein expression.

Systems and-omics Integration

  1. Metagenomics: Analyzing Microbial Communities This topic will explore the study of genetic material recovered directly from environmental samples. Students will learn the bioinformatic techniques used to analyze metagenomic data, identify microbial species, and understand the functional potential of microbial communities.

  2. Functional Genomics and Gene Ontology This section will focus on methods to understand the function of genes and proteins on a genomic scale. It will introduce the concept of Gene Ontology (GO) and other functional annotation databases, and how to perform enrichment analysis to interpret large gene lists from ‘omics’ experiments.

  3. Biological Networks and Systems Biology This topic will introduce the concept of representing biological data as networks. Students will learn about different types of biological networks (e.g., protein-protein interaction networks, metabolic networks) and how to use network analysis to gain insights into complex biological systems.

Advanced and Applied Topics

  1. Introduction to Machine Learning for Bioinformatics This forward-looking topic will provide a basic introduction to machine learning concepts and their applications in bioinformatics. It will cover supervised and unsupervised learning methods for tasks such as classification of disease samples, prediction of protein function, and identification of biomarkers.

  2. Clinical Bioinformatics and Personalized Medicine The final topic will explore the application of bioinformatics in a clinical setting. It will cover topics such as the analysis of human genomic variation, the identification of disease-associated mutations, and the use of genomic data to guide personalized treatment strategies.